Time Series for Data Science: Analysis and Forecasting
暫譯: 數據科學中的時間序列:分析與預測
Woodward, Wayne A., Sadler, Bivin Philip, Robertson, Stephen
- 出版商: CRC
- 出版日期: 2022-08-01
- 售價: $4,820
- 貴賓價: 9.5 折 $4,579
- 語言: 英文
- 頁數: 506
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 036753794X
- ISBN-13: 9780367537944
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相關分類:
Data Science
海外代購書籍(需單獨結帳)
商品描述
Data Science students and practitioners want to find a forecast that "works" and don't want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble modelling for combining information from several strategies. Covering time series regression models, exponential smoothing, Holt-Winters forecasting, and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject.
This book is an accessible guide that doesn't require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed.
Features:
- Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing, Holt Winters, ARMA and ARIMA, deep learning models including RNNs, LSTMs, GRUs, and ensemble models composed of combinations of these models.
- Introduces the factor table representation of ARMA and ARIMA models. This representation is not available in any other book at this level and is extremely useful in both practice and pedagogy.
- Uses real world examples that can be readily found via web links from sources such as the US Bureau of Statistics, Department of Transportation and the World Bank.
- There is an accompanying R package that is easy to use and requires little or no previous R experience. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use.
商品描述(中文翻譯)
資料科學的學生和從業者希望找到一個「有效」的預測方法,而不想被限制於單一的預測策略,資料科學的時間序列:分析與預測討論了集成建模技術,以結合來自多種策略的信息。內容涵蓋時間序列回歸模型、指數平滑、霍爾特-溫特斯預測以及神經網絡。特別強調了經典的ARMA和ARIMA模型,這在其他相關教材中往往缺乏。
這本書 是一個易於理解的指南,不需要微積分背景即可引人入勝,但也不會迴避對所討論技術的深入解釋。
特色:
- 提供對各種時間序列模型和方法的全面覆蓋和比較:指數平滑、霍爾特-溫特斯、ARMA和ARIMA、深度學習模型,包括RNN、LSTM、GRU,以及由這些模型組成的集成模型。
- 介紹ARMA和ARIMA模型的因子表表示法。這種表示法在此級別的其他書籍中並不存在,對於實踐和教學都非常有用。
- 使用可以通過美國統計局、交通部和世界銀行等來源的網絡鏈接輕鬆找到的現實世界範例。
- 有一個附帶的R套件,易於使用,幾乎不需要之前的R經驗。該套件實現了書中介紹的各種模型和方法,具有極大的教學價值。
作者簡介
Wayne Woodward, Bivin Sadler, Stephen Robertson
作者簡介(中文翻譯)
韋恩·伍德華德 (Wayne Woodward)、比文·薩德勒 (Bivin Sadler)、史蒂芬·羅伯遜 (Stephen Robertson)